#  Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
import unittest
import sys

sys.path.append("..")
from op_test import OpTest
import paddle
import paddle.fluid as fluid

paddle.enable_static()


class TestMeanOp(OpTest):
    def set_mlu(self):
        self.__class__.use_mlu = True
        self.place = paddle.device.MLUPlace(0)

    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")}
        self.outputs = {'Out': self.inputs['X'].mean(axis=0)}

    def test_check_output(self):
        self.check_output_with_place(self.place)

    def test_check_grad(self):
        self.check_grad_with_place(self.place, ['X'], 'Out')


class TestMeanOp5D(TestMeanOp):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.inputs = {
            'X': np.random.random((1, 2, 5, 6, 10)).astype("float32")
        }
        self.outputs = {'Out': self.inputs['X'].mean(axis=0)}


class TestMeanOp6D(TestMeanOp):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.inputs = {
            'X': np.random.random((1, 1, 2, 5, 6, 10)).astype("float32")
        }
        self.outputs = {'Out': self.inputs['X'].mean(axis=0)}


class TestMeanOp8D(TestMeanOp):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.inputs = {
            'X': np.random.random((1, 3, 1, 2, 1, 4, 3, 10)).astype("float32")
        }
        self.attrs = {'dim': (0, 3)}
        self.outputs = {'Out': self.inputs['X'].mean(axis=(0, 3))}


class Test1DReduce(TestMeanOp):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.inputs = {'X': np.random.random(120).astype("float32")}
        self.outputs = {'Out': self.inputs['X'].mean(axis=0)}


class Test2DReduce0(Test1DReduce):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.attrs = {'dim': [0]}
        self.inputs = {'X': np.random.random((20, 10)).astype("float32")}
        self.outputs = {'Out': self.inputs['X'].mean(axis=0)}


class Test2DReduce1(Test1DReduce):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.attrs = {'dim': [1]}
        self.inputs = {'X': np.random.random((20, 10)).astype("float32")}
        self.outputs = {
            'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
        }


class Test3DReduce0(Test1DReduce):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.attrs = {'dim': [1]}
        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float32")}
        self.outputs = {
            'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
        }


class Test3DReduce1(Test1DReduce):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.attrs = {'dim': [2]}
        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float32")}
        self.outputs = {
            'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
        }


class Test3DReduce2(Test1DReduce):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.attrs = {'dim': [-2]}
        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float32")}
        self.outputs = {
            'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
        }


class Test3DReduce3(Test1DReduce):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.attrs = {'dim': [1, 2]}
        self.inputs = {'X': np.random.random((5, 6, 7)).astype("float32")}
        self.outputs = {
            'Out': self.inputs['X'].mean(axis=tuple(self.attrs['dim']))
        }


class TestKeepDimReduce(Test1DReduce):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")}
        self.attrs = {'dim': [1], 'keep_dim': True}
        self.outputs = {
            'Out': self.inputs['X'].mean(
                axis=tuple(self.attrs['dim']), keepdims=self.attrs['keep_dim']
            )
        }


class TestKeepDim8DReduce(Test1DReduce):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.inputs = {
            'X': np.random.random((2, 5, 3, 2, 2, 3, 4, 2)).astype("float32")
        }
        self.attrs = {'dim': (3, 4, 5), 'keep_dim': True}
        self.outputs = {
            'Out': self.inputs['X'].mean(
                axis=tuple(self.attrs['dim']), keepdims=self.attrs['keep_dim']
            )
        }


class TestReduceAll(Test1DReduce):
    def setUp(self):
        self.set_mlu()
        self.op_type = "reduce_mean"
        self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float32")}
        self.attrs = {'reduce_all': True}
        self.outputs = {'Out': self.inputs['X'].mean()}


if __name__ == '__main__':
    unittest.main()
